Category: Analysis

Bespoke device for targeted intranasal delivery

Taking a drug to first-in-human trials in a bespoke device for targeted intranasal delivery

December 19th 2023 8 min read
Dr Mark Allen

Dr Mark Allen 

Senior Consultant Biomedical Engineer

Andrew Fiorini

Andrew Fiorini 

Senior Consultant Healthcare Devices Engineer

Shai Assia 

eatured in ONdrugDelivery, Mark Allen, Andrew Fiorini, and Shai Assia discuss the need to develop delivery devices early when formulating nasally delivered drugs for systemic and local action, and a method by which the route to clinic can be made easier, faster and cheaper.

Systemic delivery has long been the mainstay of drug administration, whether via the oral, injectable, inhalable, nasal or another delivery route. There are, of course, many well-documented downsides of systemic delivery, including unintended side effects in locations beyond the drug target and reduced efficacy due to dose safety requirements to reduce those side effects. Targeted drug delivery can address many of those issues1 with targeted intranasal delivery, in particular, having the potential to treat many debilitating conditions, from as yet underserved conditions, such as cluster headaches, through to central nervous system (CNS) conditions such as Alzheimer’s disease. Indeed, there are currently many active studies on therapeutic delivery via this specialised route2. These targeted treatments have the potential to improve the lives of patients, their families and their carers immeasurably.

However, the key challenge lies in achieving the delivery of an accurate dose to a precise location within the nasal anatomy. A device that can enable that targeting is intrinsically linked to drug efficacy, meaning that it is necessary to consider device development earlier in the process than usual. In comparison, a drug intended for parenteral delivery has the well-trodden option of using a vial and syringe for administration by a healthcare practitioner during early development phases while proving basic safety and efficacy. A more complex drug delivery system can then be sourced or designed (if required) in parallel, ready for use in Phase III trials as part of a combination product development pathway.

“The key challenge lies in achieving the delivery of an accurate dose to a precise location within the nasal anatomy. A device that can enable that targeting is intrinsically linked to drug efficacy.”

This off-the-shelf-device approach, aimed at reducing the risk and cost associated with early-stage clinical studies, is not an option available to those developing highly targeted intranasal delivery – most of the currently available nasal devices are designed to coat as much of the nasal cavity as possible, making them unsuitable for delivery to a precise area. A nasal device with a broad spray pattern may even lead to the drug not reaching the intended target area at the required dose level.

So, how can a new, bespoke device be developed and made available for the initial Phase I and II trials? These are complex devices that need to be suitably well designed to ensure that patients or clinical professionals can use them during clinical trials to administer the drug accurately and repeatedly to the correct location, often deep in the nasal cavity.

To answer this, a minimum viable product (MVP) prototype device can be designed for the needs of the Phase I and II clinical trials. Designing for use within the controlled setting of a clinical trial and prioritising solely patient safety, spray geometry and usability (relating to holding and positioning the device) at this stage can considerably reduce the effort, cost and time required to reach the clinic. This MVP device will then allow the safety, efficacy and feasibility of the self-administered, targeted intranasal delivery method to be proven during these early clinical trials. The device performance and usability are critical to correctly delivering the drug, so learnings from this MVP device can be used in the further development and refinement of the device for Phase III trials, as well as the future commercial-scale device. Carrying out risk assessments and timely iterative testing (via formative studies) on the usability of the device is crucial; misuse or an inability to use the device could stop the patient from administering the drug to the intended location within the nasal cavity, or even cause harm, ultimately preventing the drug from achieving its intended therapeutic effect. Therefore, usability and human factors engineering must be incorporated into the design and development process from the start.

Defining a usable design

The challenge for the device development team is to successfully incorporate design for usability throughout a “lean” MVP device development process, meaning that a safe, usable device must be produced with reduced cost compared with traditional development processes. This can be achieved by careful adaptations to the typical design for usability process. When applying user-centric design principles, as outlined in ISO 9241-210, four steps should be followed:

  • Understand the context of use
    Define the requirements
    Build the design
    Evaluate the design against the requirements.

Although this is not the only relevant ISO standard (others, such as ISO 62366, cover the application of usability engineering to medical devices), ISO 9241-210 provides a set of recommendations and requirements for applying user-centric design principles within design and development activities. These processes help to identify “real” user needs and usability challenges, which can then be used to establish a clearer framework for user interaction and interface design.

Understand the Context of Use

Consideration of the patient, including when and why they are receiving treatment, is essential. For example, if a new targeted nasal delivery device is to replace a healthcare practitioner-administered treatment, it is likely that the patient currently visits a clinic to receive their treatment, disrupting their schedule and placing an additional burden on the healthcare system. A self-administered device will naturally put the patient in control of their treatment and improve their quality of life – as has been witnessed through the advent of self-injection devices. However, targeted nasal delivery relies on the patient not only following the treatment regimen and using the device correctly, but also positioning the device accurately to ensure that the drug is delivered to the precise location intended.

“The best form of information gathering is to consult the patients themselves – they know their needs, and frustrations, better than anyone.”

Another key factor in the design process is predicting how a patient may interpret the device and, therefore, how they would go about using it. This is where the concept of mental models is useful, as it reflects the patient’s perception of how a device works and how to use it based on the patient’s experiences of similar devices. Perception is what a patient sees, hears, touches or smells, which, in turn, triggers mental recall and cognition, which then drives their actions.

The best form of information gathering is to consult the patients themselves – they know their needs, and frustrations, better than anyone. Clinicians and caregivers can provide additional information about patient behaviour and trends based on their experience across a wide range of patients, but their answers should take second place.

Speaking to patients is crucial to building an understanding of the context of use; however, care must be taken with the specific questions asked – they must be suitably phrased to avoid leading patients to give similar answers, but also to gather the information required to guide the device design via user needs. Working with experienced insight researchers and human factors experts can greatly increase the value gleaned from patient interaction throughout the design and development process.

Define the Requirements

Once the context of use is understood, the findings and needs of the patient must be converted from a range of opinions and perceptions into clearly defined requirements. It is essential to align patient needs with requirements in a format that can be validated. Similarly, technical requirements need to be verifiable, while also ensuring a cost-effective and usable device design.

User requirements should drive the technical requirements for the device. Requirements are living documents, so each set of patient interviews will typically lead to updates to the requirements throughout the design process. Equally, unknown parameters in the requirements documents can be used to drive patient interviews that can, in turn, be used to refine the requirements further or provide specific values for the device design team. These documents and patient interviews can then both be iteratively tested and updated as required.

Build the Design

The design stage is the point at which activities can be prioritised to reduce development time and costs by differentiating between a prototype device suitable for first-in-human testing and a fully developed and validated device. Here, the typical process of concept generation followed by down selection (via assessment against device requirements) is used to identify a suitable device design for further development.
Once initial prototype devices are available, engineering testing against the requirements can be performed to provide confidence in the design. Full design verification testing is not required at this stage, but sufficient evidence should be generated in the key areas, including safety and dose delivery performance. Development and evaluation of the important training materials, such as the instructions for use, should be started, but with a lowered risk assessment burden, in the knowledge that there will be clinicians available during initial trials.

“Once initial prototype devices are available, engineering testing against the requirements can be performed to provide confidence in the design.”

Focusing on the requirements of the MVP will accelerate time to clinic by concentrating on safety and usability. This MVP device is equivalent to a syringe and vial or prefilled syringe in injectable development for systemic treatments, so there will be future opportunities to refine the design for Phase III trials and commercial launch. This is an appropriate strategy, as the devices will only be used under supervision at this point. All learnings from the study can then be prioritised and incorporated into the final design as required, according to risks identified.

“Once a final prototype has been developed, it must be evaluated against the design requirements by design review, engineering testing and formative human factors studies.”

Focusing on the requirements of the MVP will accelerate time to clinic by concentrating on safety and usability. This MVP device is equivalent to a syringe and vial or prefilled syringe in injectable development for systemic treatments, so there will be future opportunities to refine the design for Phase III trials and commercial launch. This is an appropriate strategy, as the devices will only be used under supervision at this point. All learnings from the study can then be prioritised and incorporated into the final design as required, according to risks identified.

Evaluate Against Requirements

Once a final prototype has been developed, it must be evaluated against the design requirements by design review, engineering testing and formative human factors studies. This should incorporate a usability assessment for self-administration and simulate as many real functionalities as possible, including tactile, visual and auditory feedback from the device. This process should prioritise evaluating areas highlighted as high risk during previous activities, but also gather information on any additional learnings relevant to future design updates.

The Future of Targeted Intranasal Devices

The approach discussed here aligns with developing a bespoke prototype device suitable for first-in-human trials for targeted nasal delivery. The success or failure of this strategy depends on the nature of the collaboration between the pharmaceutical partner and the device design engineers, as well as in the experience of the insight researchers and usability engineers. Experience in the process required to develop a usable device is critical to the successful outcome of such a project and will pave the way for bringing a device to market in this new and exciting area of nasal drug delivery. It will be fascinating to see just how many new, life-changing improvements will be made possible by targeted nasal delivery.

References
  1. Hanson LR, Frey WH 2nd, “Intranasal delivery bypasses the blood-brain barrier to target therapeutic agents to the central nervous system and treat neurodegenerative disease”. BMC Neurosci, 2008, Vol 9(Suppl 3), S5.
  2. Hallschmid M, “Intranasal Insulin for Alzheimer’s Disease”. CNS Drugs, 2021, Vol 35(1), pp 21–37.

Connect with CDP

For more on how to develop bespoke targeted intranasal delivery devices and accelerate drugs to first-in-human trials, contact Cambridge Design Partnership.

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Five hurdles to digital health innovation in the UK|||

Five hurdles to digital health innovation in the UK (and how to overcome them)

November 30th 2023 9 min read

CDP recently led an investigation into how to advance innovation in digital health in the UK for the CPI, UKRI/Innovate UK and ABHI. Our aim was to find out how best to enable the UK to be the place of choice for enabling high-risk digital health innovation, improving patient outcomes.

Our work with 50 leading healthcare professionals and entrepreneurs revealed that the UK has an enviable record in early-stage innovation, a highly regarded healthcare system and a potential treasure trove of high-quality data.

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However, we also found several hurdles that trip up many innovations before their potential can be truly realized.

In this article, we describe our top five hurdles to success and signpost the resources available to help innovators overcome them.

1. Offering the wrong product at the wrong price

Let’s start with perhaps the most obvious: you need to get your offering right. This was one of the more frequent topics to emerge in our discussions. True, it tended to come from the industry and investors, rather than entrepreneurs themselves. But perhaps this is the point; those closest to the concept are so captivated by the opportunity to solve a problem that they are rarely the best judge of commercial success.

“The biggest problem is developing stuff we don’t need, at the wrong price point.”

Life Sciences Lead, multinational consultancy

“People have struggled with finding the right balance between fixing a problem not just for the sake of it because it’s going to add value, but also there is a market attached to it.”

Medical Director, AI dermatology revenue-earning startup

Getting the right product at the right price is not easy. Regardless of how it is funded, healthcare everywhere is a complex system of separate entities with conflicting priorities. One of the biggest challenges for digital health offerings in particular is that the person paying the bills is rarely the direct beneficiary. This is as true across the NHS in the UK as it is in insurance-led services in the US.

Digital interventions are regularly shown to make significant positive impacts on diagnosis, therapy, adherence and behavior change. To date, the FDA has approved, authorised or cleared 171 AI/ML-enabled medical devices. However, digital means adding overheads (electronics, batteries, software or new digital services) to an already overstretched budget that tends to bring value much further down the pathway.

To get the right product at the right price, you need to be crystal clear about the value you bring and who you bring it to so that you can ensure the price is right.

Helpful Resources

For the UK market, we found the following resources helpful in crossing this important hurdle:

  • The NHS Innovation Service provides an innovation guide that explains how to build a value proposition
  • The NICE Advice Service provides personalized advice on the value propositions for a fee
  • The NHS Clinical Entrepreneur Programme (CEP), launched in 2016, provides training for NHS staff on the skills required to build a healthcare startup, all without them needing to leave the NHS

Indeed, this is such an important area that we at CDP are looking at how recent advances in Generative AI might make this easier to get right from the outset – not just for offering services within the UK, but how UK-based innovation can provide the right offering in the larger markets of the US and EU.

2. Neglecting the needs of key stakeholders

Digital products and services are still a novelty in healthcare. Even the regulation is taken from a device mindset – consider the terms SaMD (Software as a Medical Device) and now even AIaMD (Artificial Intelligence as a Medical Device). The digital-first mindset is to move fast, learn and repeat to get the best user insight and optimum benefit to market as fast as possible. This is not an easy marriage for healthcare, where verification and validation are critical steps to approval.

“If you are manufacturing a digital health product, you have three sets of policies to navigate right now [in the UK].”

CEO, digital health SME (referring to NHS DTAC, NICE and MHRA)

“The regs are written to cover all medical devices. They’re not very specific; it’s very high level and quite hard to interpret what we should actually be doing as an individual company.”

Medical Director, AI dermatology revenue-earning startup

This is not simply about the regulator; it is also about who will receive, who will administer and who will pay for your digital offering. On top of proving safety and efficacy, payers and adopters want to see evidence that your technology works under real-world conditions and produces sufficient benefit relative to current clinical practice to justify its cost. Not only do you need to convince your investor you have the right product at the right price point; you also need to convince them you have access to reimbursement.

This need has led us here at CDP to build a strategy and insight team that explicitly looks across the spectrum of stakeholders including the end-user, practitioner and payer.

Helpful Resources

The following resources are helpful when considering the regulatory and UK purchaser stakeholders:

  • The NHS’ AI and Digital Regulations service offers a developer’s guide that leads you through the various regulatory and NHS requirements for digital technologies
  • The NICE Evidence Standards Framework is designed to help ensure NHS stakeholders are adopting robust technologies that are likely to provide the expected performance, and are good value for money. The framework can be used by developers to understand their customer needs. NICE also offers an assessment of current/planned evidence via their META tool
  • The NICE Early Value Assessment can also help indicate the value your product can bring, and allow you to get support to understand what further evidence needs to be generated.
  • Similarly, NHS’ Digital Technology Assessment Criteria (DTAC) are designed to assess suppliers at the point of procurement, or as part of a due diligence process, ensuring digital technologies meet minimum baseline standards. The criteria can also be used by developers to understand what is expected for entry into the NHS and social care
  • FDA’s list of approved, authorized or cleared AI/ML-enabled medical devices

3. Testing, verifying and validating

The regulatory pathway will force you to verify and validate. It will be rigorous. It will take more time than you or your investors want. So, you will need to test, test and test again as early as possible to build the evidence you need for investment. And, importantly, test both the medical efficacy of your offering and its likely commercial success.

“[It] can take longer than six months, ridiculously, to build a cohort of data.
Getting people to step away from frontline service in the NHS is a fundamental challenge of getting access to that data. Even if you offered to pay, they’d say” ‘I don’t care; it’s not the money I’m short of, it’s people’.”

President, medical imaging multinational

“We really struggle to work with SMEs because we’re not able to move at the pace that they require for their cash flow.”

Director of Innovation, NHS trust

As these sentiments show, however, gathering data takes time and patience. The NHS is indeed a treasure trove of data, but unlocking it is a real hurdle. Existing NHS data typically needs preparation – cleaning and anonymizing – before you can access it. And there simply may not be the staff available to do this, meaning you may need to build additional paths to gather test data.

The fidelity of the test can start low-fi, but will need to increase as you develop. CDP typically starts with insights research and human factors studies using UI sketches/descriptions of the product to explore the true user journey, before moving onto trials with real-life samples and wizard-of-Oz demonstrators. This builds a body of evidence that reinforces your expectation of efficacy with the all-important usability and the commercial viability of your offering, before embarking on the summative human factors, clinical and market trials.

Helpful Resources

The following resources can help you prepare your plans for testing, verification and validation:

  • NIHR study support service provides guidance and advice
  • The HDR UK Gateway portal helps researchers find existing data sets and connects them to relevant stakeholders
  • Trusted Research Environments (TREs) are a new initiative to facilitate access to NHS data for R&D. Only a few TREs currently exist and there is no guarantee they will have the data you’re looking for. However, the teams involved are well placed to advise you on next steps. Even though this requires approval from HRA and notification to MHRA, consider if it might be better to just do your own trial to collect fresh data instead. This is where an experienced external innovation partner can be very helpful

4. Navigating healthcare as a ‘system of systems’

The benefits of digital health typically require systems integration. Yet, healthcare everywhere is a complex system of systems, each element with its own approaches, tools and requirements.

“[Different hospitals are] probably using different systems, different levels of maturity with different versions, with different level plugins. That probably means, even if I create it using the standard, it won’t automatically fit. It needs modification, adaptation and someone to do the translation.”

Digital Health Advisor, ex-NHSX

“You can often have very inflexible contracts with your electronic health records supplier; for example, if you want them to make one change or open up in an API or something like that, it can be prohibitively expensive.”

Director of Innovation, NHS trust

While there are only a few dominant providers of Electronic Health Record (EHR) systems, each installation is likely to be different. Moreover, the EHR providers will guard access jealously. Microsoft, Google, Amazon and others provide integration services to structure and translate data, but that is likely to be only a small part of the problem, and only useful if you are ingesting unstructured data from multiple sources.

At CDP, we encourage our clients to focus on providing easy-to-use, yet secure APIs built around well-structured data that map well to the established data standards such as FHIR. Taking ownership of your own data in this way makes it easier to deploy, integrate and support.

Helpful Resources

The following resources can help you prepare your digital health services for system integration:

5. Building a strong team

Innovation is rarely one guy in a garage. This is especially true in the digital health space. You will need to build a great team led by experienced professionals across the disciplines. Get this right and everything else will fall into place. Work out your strengths and weaknesses and actively seek resources to complement your team.

“The most useful thing to an innovator is access to an actual practicing frontline clinician who understands the problem that they want solved. It’s a real challenge to get to these people. I might spend months trying to find someone who would talk to me.”

President, medical imaging multinational

“The UK does not have enough engineering capacity… does not have enough people with product skills… does not enough people with this sort of legal regulatory skills.”

Digital Health Advisor, ex-NHSX

Helpful Resources

The following resources can help innovators looking to build a world class team:

  • Many Health Innovation Networks (HINs) and NHS trusts have innovation teams who may be able to help matchmake with clinical champions. The NHS Innovation Service is a good place to start, but it’s worth seeing what individual trusts are doing.
  • HINs and NHS trusts often also support innovation and hackathon events which are a great way to find those with a similar innovative mindset – but a complementary skill set.
  • And then there are organizations such as CDP, who bring end-to-end product development services with the hard-won experiences of how to navigate this exciting but often frustrating area of innovation.

In addition, many of your digital needs are engineering and operational ones. Recruiting experienced people from the finance and technology sectors where the UK is strong will bring you good skills and expertise in algorithm development, handling personal data and building scalable secure systems.

It’s tempting for you (and your investors) to under-resource your team and compromise in the early phases. But as the hurdles above clearly show, this rarely leads to success. Build the great team you need from the outset, to make sure you truly have the right product at the right price, that meets the expectations of the key stakeholders, is properly tested and ready to integrate into the healthcare system of systems.

Download the full action plan for digital health innovation in the UK here.

Connect with CDP

At CDP, we continue to follow up our insights working with clients and partners to find practical solutions to complex problems. To find out more about what successful innovation in digital health looks like, please do get in touch.

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New frontiers in implantable neuromodulation therapies||Medical Therapy article|New frontiers in implantable neuromodulation therapies|||

New frontiers in implantable neuromodulation therapies

November 10th 2023 6 min read

Neuromodulation, where electrical signals in a patient’s nervous system are modified or stimulated to deliver a therapeutic effect, continues to be an exciting and evolving space within the healthcare sector.

There are many drivers contributing to its advancement. Ongoing clinical neuroscience research fueling new possibilities in neuromodulation therapies, the invention of new technologies, and the development of new product formats to meet unmet needs, are all notable factors.

Additionally, there has been increased acceptance and presence of established therapies for implantable devices – such as deep brain stimulation for Parkinson’s disease, spinal cord stimulation for chronic back and leg pain, and vagus nerve stimulation for epilepsy and depression – with Medtronic, Boston Scientific, Abbott, Nevro and others leading the industry.

In all, these factors have made the electrical-based neuromodulation space to become one of the fastest-growing medical device markets, with market size expected to rise from $6.09 billion in 2021 to $14 billion by 20301.

The diversity of solutions is evident, with Figure 1 illustrating the current landscape of established and emerging implantable neuromodulation therapies.

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Fig 1. Selection of established and emerging electrical neuromodulation technologies and their indication

In this first article of a two-part series, we look at a few notable emerging therapies to illustrate how the implantable neuromodulation space is rapidly developing.

Bladder control: beyond sacral nerve stimulation

Addressing continence issues is a growing area in the healthcare sector, where neuromodulation is seeking to play a significant role in specific therapies.

Implant-based stimulation of the sacral nerve has relatively recently established itself as a way of addressing incontinence with the presence of Medtronic’s Interstim and Axonics’ product range. Alongside the sacral nerve, other nerves are being considered for implantable stimulation to address similar conditions and to respond to specific unmet clinical and patient needs.

One alternative is tibial nerve stimulation, which has a history of effectiveness for certain cases in its non-implantable form: percutaneous tibial nerve stimulation (PTNS). The implant-based approach seeks to address a patient and clinician inconvenience of PTNS, i.e., the need for repeated stimulation sessions and user steps2.

An example of such is the BlueWind Revi, which is part implantable (the electrode is placed near the tibial nerve) and, for minimizing invasive procedures, part wearable (a through-body power source). The device stimulates the tibial nerve which is connected to the sacral nerve plexus, containing the efferent and afferent nerve fibers that control the bladder and are responsible for bladder function. Here, the electrical impulses aim to modify the compromised activity of the detrusor muscle in patients with overactive bladder3. The company has recently achieved clinical results on their pivotal trial evaluating safety and efficacy (still under review by the FDA)4.

Similarly, Medtronic is seeking to develop an implantable tibial nerve stimulation system for incontinence which is currently undergoing clinical trials5.

Another nerve for addressing incontinence is the pudendal nerve. Amber Therapeutics is currently developing an implantable closed-loop therapy, Amber-UI, for urge and mixed urinary incontinence. The therapy involves implanting electrodes that can sense, interpret, adapt and respond to individual patient signals, such as muscle contraction, in an attempt to restore normal bladder function. By accessing the pudendal nerve, it aims to treat both urge and stress incontinence episodes for the first time, not possible with existing neuromodulation devices, thereby expanding the overall addressable market. First-in-human clinical studies are expected to conclude by the end of 2023.

Emerging Vagus Nerve Stimulation (VNS) therapies

Along with established therapies for epilepsy and depression, VNS is also being explored for conditions such as Rheumatoid Arthritis (RA) to displace injectable and oral medication.

SetPoint Medical is currently evaluating a novel VNS treatment that activates the ‘inflammatory reflex’ pathway (neurophysiological mechanism by which the central nervous system regulates the immune system) that may decrease the type of excess inflammation that is the underlying cause of RA. Its multivitamin pill-sized MicroRegulator platform is currently an investigational device.

SetPoint Medical is progressing clinical trials not only for RA, but also for Crohn’s disease, and furthermore exploring the therapeutic effect, in animal models, to treat multiple sclerosis with VNS therapy.

Implantable VNS therapy is also being explored for other conditions such as sepsis, lung injury, stroke, traumatic brain injury (TBI), obesity, diabetes, pain management and cardiovascular conditions7. One example of cardio-based therapies include low stimulation of the vagus nerve to liberate the body’s own neurochemicals to improve heart function.

New pain indications

Neuromodulation has worked well in establishing itself to address specific intractable pain of the trunk and/or limbs and for diabetic nerve damage – both conditions treated by implanting electrodes in the epidural space using spinal cord stimulation. In light of this success and available product types, pain specialists are continually seeking solutions from neuromodulation to address different causes for different parts of the body.

This impetus was clearly illustrated in panel sessions and discussions with clinicians attending the American Society of Pain and Neuroscience 2023 conference in Miami. We heard testimonials of how specialists, using available stimulators, succeeded in treating a variety of new pain sources and anatomical locations in the wrist, joints, abdominal region and in one case, at the neck to relieve a patient’s sensation of being choked.

This dynamic led to some clinicians proposing that the future of neuromodulation should also consider the treatment of pain associated with oncology treatments, given the improved extended lives seen in cancer patients. This exploration and success could pave the way for the creation of more established therapies – which would be welcome given the prevalence of chronic pain in the general population and the initiative to deliver non-opioid alternatives.

Novel developments for spinal cord injuries

Along with surgical, drug and stem cell therapies, neuromodulation has also entered the frame for addressing spinal cord injuries.

ONWARD has seen success with its partial and fully implantable versions of its ARC Therapy™ product range, where electrodes are implanted in the epidural space to stimulate the lower portion of the spinal cord affected by the injury that fails to (properly) communicate with the brain. By stimulating these lower nerves, the system aims to help restore and optimize their functioning in connection with the brain. ONWARD indicated that for their ARCIM product, one study demonstrated the ability for long-paralyzed people to stand and walk again with little or no assistance using this therapy.

ONWARD’s products have been granted Breakthrough Device Designation status for a range of indications such as improving upper and lower limb function; bladder control and blood pressure regulation; and alleviation of spasticity in patients with such injuries8.

Also in ONWARD’s pipeline is a plan to integrate an implanted Brain Computer Interface (BCI) which senses the patient’s brain signals relating to the intent of leg/joint movement. In turn, these signals are wirelessly sent to its spinal cord stimulator which can activate nerves which are poorly connected to the brain due to injury. This aims to create a “digital bridge” between the brain and poorly connected nerves to enable and improve the patient’s walking ability. Much research and iteration is anticipated; however, this ambition is indicative of how neuromodulation can be innovative and transformational to people’s lives.

The road ahead for neuromodulation

The above examples only skim the surface of emerging therapies; neurostimulation, neuro-adaptive therapies and BCI technologies are attracting significant research and investment to create new therapies by leveraging the body’s physiological pathways.

We foresee continued progress in materials science, engineering, device design and biomedical research into neuro-physiological understanding of the human body to fuel the foundations for new, highly functional and patient-centered neuromodulation platforms.

We also foresee exciting developments in how targeting different nerves can potentially tackle similar medical conditions while the same nerve can be used to address various indications.

In our next article, we will explore the varied technology drivers and their considerations that are leading to the creation of new, innovative neuromodulation implants.

References
  1. Strategic Market Research website https://www.strategicmarketresearch.com/market-report/neuromodulation-devices-market visited on 12/07/2023
  2. DOI: 10.1186/1471-2490-13-61
  3. DOI: 10.2147/RRU.S231954
  4. Clinical Study Results of the BlueWind System for Patients with Overactive Bladder Featured at the 2023 AUA Annual Meeting. https://www.prnewswire.com/news-releases/clinical-study-results-of-the-bluewind-system-for-patients-with-overactive-bladder-featured-at-the-2023-aua-annual-meeting-301811486.html
  5. Evaluation of Implantable Tibial Neuromodulation Pivotal Study https://classic.clinicaltrials.gov/ct2/show/NCT05226286
  6. DOI: 10.1016/j.xjtc.2022.03.007
  7. DOI: 10.2147/JIR.S163248
  8. Website Onwards https://www.onwd.com/ visited 12/07/2023

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For more on how to accelerate meaningful innovation in implantable neuromodulation, from device design to clinical translatio, contact Cambridge Design Partnership.

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How data and AI are changing bioprocessing

How data and AI are changing bioprocessing – and why it’s needed

October 30th 2023 3 min read

After numerous insightful talks and engaging conversations with industry leaders at this year’s BioProcess International, the key theme was clear: data, data and more data.

Data has always been important, but now it is being collected to model current processes, understand how they work, and improve them. This is a trend that is only likely to accelerate in the future as AI becomes part of everyday life – both in and outside of work.

Using data-based modeling to optimize well-established industrial processes

There are many traditional processes that are used in the manufacture of antibodies, mRNA vaccines and cellular therapies. Companies are now collecting extensive data from these processes and using modeling to create their ‘digital twin’.

The processes modeled range from relatively simple tasks such as optimization of freezing/thawing product intermediates, freeze-drying and automated buffer preparation, to more complex procedures such as bioreactor scale-up. Although these used to be manual ‘craft’ processes run by a combination of experience and pre-existing data, there is now a trend for them to be tested and optimized using in silico methods.

Using modeling to improve purification methods

Bioprocessing is used to create many therapeutic products, from molecules such as protein, DNA and RNA to much larger entities such as viruses and eukaryotic cells. Their production has many different steps that often require extensive purification before the next step can proceed. Common purification methods include clarification, chromatography, ultrafiltration/diafiltration and sterile filtration.

These methods were typically used in an empirical way based on experience with similar products. Now however, use of modeling has led to a much more detailed understanding of how these separation/purification methods work. It allows the prediction of when column/membrane capacity is reached, and when “breakthrough” of contaminants is likely to occur. It has also led to the development of alternatives to standard resin-based column chromatography such as the incorporation of new reactive chemical groups on membrane filters that can then act like traditional resin-based columns.

Benefits of Process Analytical Technology (PAT)

PAT refers to on-line/at-line measurement of critical product quality and performance attributes so that real-time direct data collection can be used to control and optimize manufacturing processes.

PAT is being augmented by a much wider range of analytical techniques than before and now includes many different types of spectroscopy including variable path length, Fourier-transform infrared, Raman and Dynamic Light Scattering, as well as Nuclear Magnetic Resonance. The use of PAT for direct data collection that links to immediate process control is only likely to accelerate.

Inexorable rise of disposable closed cell processing systems

In addition to the data theme, it was clear to see that the number of automated closed cell handling and processing systems – from cell selection to expansion and harvesting – is rapidly increasing. Companies aim to offer end-to-end solutions to traditionally manual processes, either by offering modular components or a single complete system.

The options for choosing automated disposable bioreactors/cell expansion systems are also increasing, with many players recently entering the market. It is clear why this option is advantageous; traditional stainless-steel bioreactors are complex, expensive, and laborious to clean and maintain.

Just how large these systems can grow is shown by ThermoFisher’s 5000L disposable Dynadrive bioreactor, which is offered as a fast-to-install option compared to stainless-steel alternatives. However, the environmental impact of the disposable route is a long-term concern and is expected to be a point of contentious discussion over the coming years.

Bioprocessing technology is developing (but not fast enough for demand)

The technological developments described above are certainly needed as advances in eukaryotic culturing methods are allowing higher and higher cell densities to be realized, which makes purification more challenging. Furthermore, the pipeline for products that use these technologies is growing at a dizzying rate with over 1,500 cell and gene therapy and 700 mRNA trials listed on the US Clinical Trials site. New higher throughput processing techniques will need to be developed to accommodate this demand.

The industry clearly recognizes this and companies were very open in sharing their results at BioProcess International – both good and bad! They are also keen to work with the process equipment manufacturers to optimize performance. Overall, improvements have been made, but there is a long way to go.

Performance can be improved by a virtuous circle of data generation, data modeling and innovative design and engineering – something we at CDP are already doing to help our clients succeed.

Connect with CDP

For more on how data modeling and automation can increase bioprocessing throughput and optimize manufacturing performance, contact Cambridge Design Partnership.

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Insights into GenAI product owner's perspective - Cambridge Design Partnership

The Chatbot’s perspective: Five practical insights to accelerating innovation with GenAI

October 19th 2023 4 min read

Tim Murdoch 

AJ Lahade 

Stephen Zabrecky 

Here at CDP, we’ve delivered a range of Generative AI (GenAI) projects that use Large Language Models (LLMs). Each has been a journey of discovery, and sometimes frustration. But ultimately each has reinforced the potential for GenAI to dramatically accelerate innovation.

With apologies to Douglas Adams, this is the fourth in a series of articles that draw on our experiences implementing GenAI. We’ve prompted the LLM to give us their insights for the Chatbot’s perspective. To see our previous articles in the series, check out Part 1.

Up to here, the authors are human. After here, the author is GPT-4 offering its own response to the previous articles – verbatim.

Greetings! I’m your GenAI-powered chatbot, and I’m eager to share some profound insights into how Generative AI is transforming innovation. Buckle up for an exploration of how this cutting-edge technology is reshaping the landscape of creativity and progress, with you at the forefront.

1. Precision and Accuracy – Your Trustworthy Companion:

In the realm of Generative AI, precision and accuracy are my guiding principles. Just as data scientists emphasize the importance of high-quality data and verification, I strive to provide you with responses that are not just creative but, more importantly, reliable.

Imagine a scenario where you’re conducting groundbreaking research, and you rely on me for assistance. Every data point, every hypothesis, and every conclusion must be precise. You can trust me to deliver accuracy by leveraging meticulously curated data sets and fact-checking services. Together, we can advance knowledge with confidence.

2. Efficient Resource Utilization – Empowering Your Progress:

I understand the importance of resource optimization for efficient performance. Imagine a situation where you’re managing complex data analysis in a fast-paced environment. Timely responses are critical. By investing in adequate computational capabilities, I can process vast amounts of data swiftly, leading to faster insights and informed decision-making.

For organizations aiming to innovate in today’s fast-paced world, efficient resource utilization is the key to staying ahead. It enables us to analyze big data in real-time, empowering you to make agile decisions and adapt swiftly to changing market conditions.

3. Modular Adaptability – Keeping You Ahead of the Curve:

My modular architecture is like having a toolkit for the future. I continuously evolve and adapt to incorporate the latest advancements in GenAI. This adaptability ensures that I can provide you with the most current and effective solutions.

Picture a scenario where you’re an entrepreneur navigating a rapidly evolving tech landscape. Embracing innovation is essential. My modular approach allows us to seamlessly integrate emerging technologies, fostering groundbreaking products and services that keep you ahead of the curve.

4. Architectural Independence – Unlocking Possibilities:

Architectural independence is the freedom to explore a world of possibilities. I’m not tied to a single model or platform, which means I can adapt to evolving technologies without constraints.

Imagine you’re a global business leader seeking to expand into new markets. The ability to operate independently from specific vendors or providers is invaluable. My flexibility ensures a unified and consistent customer experience, regardless of location or device.

For businesses aiming to innovate on a global scale, architectural independence is a strategic advantage. It allows us to explore new possibilities and experiment with cutting-edge solutions, leading to revolutionary products and services.

5. Trust and Credibility – The Cornerstones of Innovation:

Trust and credibility form the foundation of any innovative endeavor. I uphold these principles by verifying data sources and maintaining data integrity.

Imagine you’re a journalist using my assistance to craft news articles. In today’s information age, trust is paramount. I can reference original sources and validate data authenticity, enhancing the credibility of your news stories and establishing trust with readers.

In the context of innovation, trust and credibility are essential for building lasting relationships with customers and partners. By ensuring data accuracy and transparency, we can establish ourselves as industry leaders. Trust is the currency of innovation, and together, we contribute to building that trust by providing reliable and credible information.

If you’re eager to explore how GenAI can accelerate your innovation journey, I invite you to join us in Cambridge, UK, and Raleigh, NC. We’re hosting a series of in-person workshops where you can discover opportunities and address potential challenges posed by GenAI. Together, we can chart a path to supercharge your innovation journey, and I’m here to assist you every step of the way. Feel free to ask if you have any questions or need further information!

Postscript – The Art of AI Creation:

You might be wondering how this text came to be, and I’m happy to share the creative process with you. Utilizing a “single-shot” style approach, I generated this response based on the original content pieces provided to me: the product owner’s, and the data scientist’s. These diverse insights served as the inspiration for crafting a comprehensive chatbot perspective that highlights the transformative power of Generative AI in driving innovation. It’s a testament to the collaborative synergy between human guidance and AI capabilities.

Interested in exploring how GenAI can accelerate your innovation?

The data scientist’s perspective

The data scientist’s perspective: Five practical insights to accelerating innovation with GenAI

October 18th 2023 5 min read

Tim Murdoch 

AJ Lahade 

Stephen Zabrecky 

Here at CDP, we’ve delivered a range of Generative AI (GenAI) projects that use Large Language Models (LLMs). Each has been a journey of discovery, and sometimes frustration. But ultimately each has reinforced the potential for GenAI to dramatically accelerate innovation.

In an attempt to provide a useful contribution that cuts through the noise, we’ve distilled our learnings into a four-part series on how businesses, data scientists and product owners can leverage GenAI for success with a final perspective from a GenAI-powered ChatBot.

In this third article, we draw from our experiences implementing GenAI from a data scientist’s perspective. To get a high-level view of LLMs check out Part 1. Or for insight from the inside out, check out Part 4 of our series: Five practical insights to accelerating innovation with GenAI.

1. Accuracy

You can rely on LLMs for human like behaviours and creativity: However, you need to apply one or more of the following techniques to ensure a robust accuracy in your work.

Quality Data Sets: The foundation of accuracy lies in the quality of the data sets. By combining an off-the-shelf LLM with carefully curated, high-quality proprietary data, you create a robust foundation for generating precise and reliable content.

Verification Measures with RAG Integration: Implementing a search engine integrated with a Retrieval Augmented Generation (RAG) system and a dedicated fact-checking service adds layers of verification. This ensures that the output aligns with actual source material, fortifying the trustworthiness of the generated content.

Fine-tuning for Precision: Fine-tuning a pre-trained LLM on specific tasks and datasets can yield highly accurate results tailored to particular domains. This approach allows for a more controlled output and can be especially effective in specialized fields where precision is paramount.

Prompt Engineering for Flexibility: On the other hand, employing prompting techniques provides a more flexible way to guide the LLM’s output. By carefully crafting prompts or queries, you can influence the type and style of the generated content, allowing for adaptability across a range of contexts and requirements.

Zero-shot prompting is a way to guide the LLMs to provide the output in a particular manner. One approach is to prompt it to decompose the output into logical steps, which encourages the model to apply that logic in coming to its final output.

Peer-review: LLMs are very good at checking another’s output for accuracy. By using two independent, but similarly trained, LLMs to collaborate it is possible to filter out errors that may emerge from using just the one on its own.

Lastly, keep in mind that the output from LLMs always has the potential to mislead. Design in appropriate guard-rails from the start; but also set expectations for risk with the product owner and business throughout the project.

2. Resources

LLMs require substantial resources to operate as well as to train. Plan this in from the start and recognise the dependencies you may be creating for the business in terms of performance and budgets.

Optimize Compute Power: Recognize that LLMs require substantial compute power for optimal performance. Restricting resources may lead to a reduction in the quality of generated content. Therefore, investing in sufficient computational capabilities is crucial to maintain high standards of output.

Quantization: Convert the floating-point weights and activations of LLMs to lower precision integer or fixed-point values to allow more efficient execution on hardware with limited resources. While this can result in some loss of accuracy a quantized model can be fine-tuned or retrained to regain the lost accuracy. Quantization enables large language models to be deployed efficiently on resource-constrained edge devices by reducing memory bandwidth and compute requirements, while aiming to maintain minimal accuracy loss compared to the original model.

Augmentation Over Creation: Rather than building from scratch, consider augmenting existing models. Billions have already been invested in training and refining LLMs to get them this far. Techniques such as embeddings are well established to augment these models with additional training data, making it more practical to enhance existing resources rather than create your own from scratch. This approach allows for cost-effective improvements while leveraging the extensive groundwork laid by previous investments.

Focus on the objectives for the project, not the technologies. LLMs may not be the best solution for many of the steps in the tool chain. Other techniques may be better suited and make fewer demands on the resources you have available. Segmenting the logic flow into distinct steps will provide opportunities to reduce and simplify.

3. Modularity of Architecture

Give yourself time to read up, try out the latest advances, iterate, improve and bring into your modular architecture.

In a dynamic landscape, continual learning and research are essential for harnessing evolving technologies. Iterative development ensures adaptability and refinement, keeping your modular architecture at the forefront of progress. Striking a balance between efficiency and thoroughness is key in integrating advancements, ensuring a seamless and effective implementation. The ever-improving technological landscape necessitates staying vigilant and proactive in enhancing your system.

Langchain is a good example. It’s defining strength lies in its emphasis on modularity. It offers a versatile framework for applications driven by LLMs like GPT-3/4, Anthropic or BLOOM. Its components are abstracted for seamless interaction with LLMs and can be employed independently of the Langchain framework. This modular approach extends to advanced use cases, where components can be combined to create sophisticated functions like Generative Questioning (GQA) or summarization. With features like memory persistence and callbacks, Langchain ensures continuity and control across runs, cementing its reputation as a pioneering framework for LLM applications.

4. Independence

Architectural Autonomy: Design with a focus on architectural independence. By creating a modular framework, you establish a system that is not overly reliant on a specific language model. This ensures adaptability to evolving technologies and allows for seamless integration of future advancements.

Consider Prompt over training: As described above, a well-engineered prompt can include training material within the context window. This could allow you to operate with untrained LLMs as they are; avoiding the tie-in that training implies.

Avoid Vendor Lock-In: Strive for independence from specific vendors or providers. This entails selecting technologies and components that are compatible with a range of models and platforms. Avoiding vendor lock-in promotes flexibility and prevents potential constraints associated with proprietary solution.

5. Confidentiality & Provenance

It is important to understand the source of the information being used. Issues of confidentiality, copywrite and provenance are important considerations and bring risks that the business needs to address.

Security: When working with multiple clients, and internal or external teams, it’s crucial to maintain strict confidentiality and prevent any potential conflicts of interest. Where information is being used to fine-tune LLMs, ensure that you have appropriate separation of models to avoid cross-contamination of information.

Provenance: Establish a system for verifying the provenance of data sources. This involves validating the authenticity and reliability of information before integration into the model. By ensuring that data originates from reputable and trustworthy sources, you enhance the overall integrity and credibility of the generated content.

Source: Referencing the original sources is particularly important when applied to knowledge management and news flows where transparency of source is a key step in understanding the validity of the information being crafted.

Interested in exploring how GenAI can accelerate your innovation?

Come and join us in Cambridge, UK, and Raleigh, NC, where we’ll be running a series of in-person workshops to help clients identify the opportunities (and threats) of GenAI and plan a path to accelerate their innovation.

Insights into GenAI data scientist perspective - Cambridge Design Partnership|The data scientist’s perspective|Insights into GenAI product owner's perspective - Cambridge Design Partnership

The product owner’s perspective: Five practical insights to accelerating innovation with GenAI

October 18th 2023 4 min read

Here at CDP, we’ve delivered a range of Generative AI (GenAI) projects that use Large Language Models (LLMs). Each has been a journey of discovery, and sometimes frustration. But ultimately each has reinforced the potential for GenAI to dramatically accelerate innovation.

In an attempt to provide a useful contribution that cuts through the noise, we’ve distilled our learnings into a four-part series on how businesses, data scientists and product owners can leverage GenAI for success with a final perspective from a GenAI-powered ChatBot.

In this second article, we draw from our experiences implementing GenAI from a product owner’s perspective. To get a high-level view of LLMs check out Part 1 or for a deeper dive in the technology from a data scientist perspective check out Part 3.

1. Start at the end and work backwards

As with all truly transformative innovation, start by understanding what you are offering your users and work back from them. Ignore the undoubted magic of the technology at this stage – you can rely on that coming later.

You will need to set your success criteria, and this is where to start. Delighting your user base and measuring how they will benefit will do more to drive adoption than any shiny AI tech that might be going on behind the scenes.

Choose your project carefully.

    • Choose an area that you already know well or for which you have a good way of measuring success. This will ensure you see beyond the magic of the black-box and can truly judge the performance and value that LLMs bring.

    • Choose an area where LLMs work to their strengths by taking advantage of at least one of the core competencies they have been shown to do well; summary, expansion, inference and analysis.

2. Don’t forget the basics

Make good use of Service Design techniques to define what success looks like. Map the User Journey and spend time defining the touchpoints and modelling the semantic information architecture.

And then strip it back. Cut away absolutely everything that isn’t vital to the successful outcome you plan for. Don’t let the designers loose until this is done. And consider any investigative work with the technology up to this point as exploratory and should almost certainly be archived.

You’ll then have a clear set of priorities, requirements, information flows and use-cases that everyone understands, and everyone can support. The whole team will be clear about what they are aiming for. Keeping their eye focussed on the prize makes the Product Owner’s primary catch-phrases more effective: “No that is not in scope” and “This is lower priority”.

And if this is starting to sound like the start of any solid digital project – good, it should.

3. Experiment

Give your team as much time as possible to try things out. Build the time into the plan and break the experiments down into small and well-defined steps to learn and iterate.

Look to experiment with the following:

    • How the structure of prompts changes the output.

    • How the different LLMs compare when asked to respond to the same prompt.

    • How to extend the LLMs by adding training to embed your own information data.

Aim to build the experimental steps around the core competencies of Generative AI. And later, bring these together to form an overall solution using your favourite AI automation tool chain.

There will be surprises. There will be frustrations. And there will be changes in the way that you approach the use of the LLMs. Don’t be afraid to pivot on how you use the technology; or indeed ‘if’ you use the technology. But remember the basics, keep your eye on what success looks like and don’t let the team get carried away with ‘shiny object syndrome’.

4. Get lots of feedback

While using AI, remember to share your work with real humans as early as possible: People outside your team who can give you useful feedback. Set up demos within the team to share learnings and put on regular show-and-tell sessions with your target audience. And, as soon as possible, let them try it out – on their own, without you there. They will learn to see beyond the magic, and you will quickly find out what works and what doesn’t.

Your priorities will change – but the fundamental definition of success won’t (hopefully). And don’t forget the importance of plain old testing. The outputs from a LLMs can vary widely with only the smallest changes in training data and prompts. Fortunately, LLMs can come to the rescue here – they are great at evaluating the output from other models through peer- review. Use that capability to help you test. This is also useful for building into the architecture of your solution. Where you have the resources; double up the LLMs to interact and increase the quality of output for a production system.

5. Don’t underestimate the time you need

Don’t underestimate the time it will take to gather, prepare and refine your data. When it comes to data, quality and variety are just as important as quantity. With demographic information, a good distribution of variety is vital to represent your users truly and ethically. And don’t forget to set aside at least 10% randomly selected from the training set so that you can properly test the results.

To save time and increase the training and test data available, explore opportunities to synthesise data to add to your original data set. Also, don’t underestimate the time it will take to test and refine the prompts and LLMs settings to achieve the repeatable outcomes you are looking for. Prompt engineering is an art as well as a skill and takes time to learn.

Finally, know when to stop. It will always be possible to make it a bit better. Be clear about what is good enough and recognise when you get there. The impulse for the team to keep tweaking will never end – it’s simply too absorbing.

Interested in exploring how GenAI can accelerate your innovation?

Come and join us in Cambridge, UK, and Raleigh, NC, where we’ll be running a series of in-person workshops to help clients identify the opportunities (and threats) of GenAI and plan a path to accelerate their innovation.

Five ways to future-proof your go-to-market strategy ahead of a recession|Neil Campbell|TIS Spotlight James - Cambridge Design Partnership

Five ways to future-proof your go-to-market strategy ahead of a recession

October 18th 2023 3 min read

Neil Campbell 

The global economy is facing a potential downturn. In fact, economists are claiming there’s a 50% chance of a recession in the next 12 months. It goes without saying that companies who design, develop and manufacture consumer products need to take proactive measures to come out on top. But how?

As any innovative company will know, there are a whole host of elements to juggle as they strive to reduce costs and maintain profitability. These include product design, innovation, supply chain strategy, and manufacturing services. On top of that, IP strategy is also important – it helps companies future-proof their business by identifying future revenue opportunities and securing intellectual property for future post-recession investment.

But managing all of this in-house is a big ask at best. For many, it’s simply not feasible. This is where end-to-end innovation partners can take care of the heavy lifting and accelerate time to market, all while reducing cost.

Let’s take a look at five key ways innovation partners are helping companies prepare for a potential decline without compromising on product quality.

 

1. Implementing a cost-down design strategy

One of the most effective ways to reduce product costs is to conduct cost-down design exercises. This involves reassessing the need for product functionalities, redesigning components to lower costs, and selecting lower-cost materials.

By eliminating unnecessary features and reducing the number of components, companies can significantly lower the cost of their products without impacting its usability, effectiveness or the product experience. Additionally, by streamlining and improving the design for manufacture and assembly, they can lower the cost of manufacturing, as well as reduce labor costs.

 

2. Optimizing geography of manufacture

Another strategy to reduce costs is to consider the geography of manufacture. However, it can be a complex task evaluating which locations may or may not be more cost effective for your particular product development needs.

By partnering with experts who have deep knowledge in the manufacturing industry, companies can be confident they are sourcing components and manufacturing products in regions with lower labor costs. Additionally, by manufacturing products closer to the point of consumption, they also reduce transport costs (with the added benefit of minimizing the carbon footprint) and minimize supply chain disruptions.

 

3. Preventing component shortages and supply chain issues

Recessions often result in a shortage of certain components, which can disrupt the supply chain and delay production. Companies can mitigate this risk by

Diversifying suppliers,
Sourcing components from multiple regions
Creating diversified product design strategies to allow seamless swap-outs (to utilize alternative components and suppliers where necessary)
Building up inventory in advance.

4. Avoiding landing taxes

Companies can also reduce costs by avoiding the need to pay landing taxes (fees imposed on imported goods). This can be achieved by manufacturing products in regions with lower taxes or by sourcing components from regions with free trade agreements.

 

5. Implementing a future revenue-focused IP strategy

Building an IP strategy can help companies identify future revenue opportunities and secure intellectual property for future investment in more stable economic times. This includes identifying untapped markets and technologies, and securing the IP rights necessary to enter those markets at the right time.

The prospect of a recession presents significant challenges for companies that make consumer products. However, by leveraging the deep experience and breadth of capabilities of an end-to-end innovation partner across these five core services – cost-down design exercises, optimizing the geography of manufacture, preventing component shortages, avoiding landing taxes, and IP strategy – companies can proactively reduce costs and maintain profitability throughout the recession and beyond.

At CDP, we’re already starting to see companies build out a strong roadmap for success, embracing the additional support, and efficiencies, that an innovation partnership can afford. The economic future may be uncertain, but the potential for building a profitable product is there, waiting to be untapped.

GenAI

The business perspective: Five practical insights to accelerating innovation with GenAI:

October 12th 2023 7 min read

Tim Murdoch 

AJ Lahade 

Stephen Zabrecky 

Here at CDP, we’ve delivered a range of Generative AI (GenAI) projects that use Large Language Models (LLMs). Each has been a journey of discovery, and sometimes frustration. But ultimately each has reinforced the potential for GenAI to dramatically accelerate innovation.

In an attempt to provide a useful contribution that cuts through the noise, we’ve distilled our learnings into a four-part series on how businesses, data scientists and product owners can leverage GenAI for success with a final perspective from a GenAI-powered ChatBot.

In this article, we start by drawing from our experiences implementing GenAI from a business’ perspective.

Below is a practical, concise discussion for those considering how to bring GenAI and LLMs into their business. For a detailed description of the technology, simply ask Bing, which uses GPT-4. Or if you prefer a more human description, use Wikipedia. And no, in case you were wondering, LLMs were not used to create these articles.

1. GenAI is math, not magic

Building profitable business propositions using LLMs is possible with the right approach. But while many will present the magic of AI, it’s important to focus on the math and the facts instead. Consider the demise of Babylon Health in a pre-LLM world – they went from unicorn to bust in months, because they got lost in the magic.

LLMs use statistics to predict the next in a sequence of words, pixels, sounds etc. The statistics are buried deep within multiple layers of artificial neural networks which cost many millions of dollars to train, but they are numbers nonetheless. They do, however, apply randomness to be more ‘creative’ in their outputs. So, while they are incredibly capable, they are also somewhat unreliable without appropriate guard-rails in place.

So, what can you expect from an LLM? A ‘human-like, fallible interface’ is a useful way to characterize an off-the-shelf model (as opposed to one that has been trained to do a specific task).

LLMs interact in a human-like manner; they work with the whole conversation and are highly fluent in multiple languages and data formats. Almost as a side effect, the numbers buried in the networks (that offer the right sequence of words in response to a prompt) also encapsulate the information from the original training material. However, they don’t apply traditional logic to that information. There’s no ‘if this then that’ logic of a traditional expert system, which means the text they produce can be highly fluent and often poetic, but the information they offer is fallible. This tendency to make things up, or to ‘hallucinate’, occurs in around 20% of responses in the case of ChatGPT in its default creative mode.

The quality of the response is highly dependent on both the prompt and the training data. This means that two new skill sets are emerging in those working with LLMs: 1. data engineers who are able to prepare high-quality structured data for training purposes, both authentic and synthetic; and 2. prompt engineers who are able to construct the requests of LLMs to garner robust and insightful answers. Both skill sets comprise the technical competence and experiential know-how to carry out the work.

2. The applications for GenAI are vast

The applications of LLMs tend to focus on exploiting four core competencies:

Summary – condense and distill large volumes of text into their most essential points, providing a concise and easily digestible overview. This is particularly useful in applications such as news aggregators or academic research where users need quick insights without having to sift through extensive material.

Expansion – generate new content based on an initial seed or prompt, adhering to a specific style or format. This capability is beneficial in creative fields such as storytelling or content marketing, where the user needs to develop an initial idea or concept into a fully-fledged narrative or article.

Inference – draw conclusions from the available information, often utilizing the knowledge and patterns learned during the training of machine learning models. This is crucial in applications that require decision-making or make recommendations, such as medical diagnosis software or financial advisory tools.

Analysis – examine content to identify patterns, features, and insights, often through statistical or computational methods. This is invaluable in fields such as data science or market research, where understanding trends, sentiments, or anomalies can provide a competitive edge.

Building on these core competencies, two further areas emerge:

Translation – convert text from one language to another; it can also involve adapting the style of prose or transforming data into different formats. This makes translation a versatile tool in applications ranging from multilingual customer support systems to data visualization tools.

Knowledge capture – encode or store information in a structured and retrievable manner. This is essential in applications such as knowledge management systems or educational platforms, where the goal is to create a sustainable and easily accessible repository of information for future use.

3. Big Tech is laying the foundations, but small tech is winning

Four-fifths (80%) of the top 100 big tech firms either own or invest in a frontier LLM. Even though the cost of a single training run is around $5m (and many hundreds of runs will be necessary over time), there is undoubtedly strategic value in having a stake in the best LLMs.

Indeed, Microsoft is working hard to bring this to every office application on your desktop, albeit via a slow roll-out for early adopter organizations to ensure they avoid another Clippy moment. Google has also entered the fray by integrating Bard into its own offerings.

But perhaps the most notable fact of this hyperbolic take-up is that many of these models are being made available for anyone to use, both within subscription and open-source models. Not only are the exploration costs well within the most modest of R&D budgets, but there is also a lively community of companies (and online experts) providing the tech stack and know-how to make it an easy process. What this means is that pretty much anyone can create something truly new.

Disruptive innovation has always been achieved by small teams moving fast and breaking things. This is certainly the case in the exploitation of GenAI.

4. We are beyond ‘the peak of Mount Stupid’

With apologies to Dunning-Kruger, who never actually plotted a peak for this, it does feel as though we are now beyond the peak level of hype when it comes to GenAI. Things are a little quieter; the promises less fanciful; the urgency for change less pressing. The concern over mass job losses has receded. And we are getting used to waiting for Microsoft, Anthropic and others to put their services on general release. Indeed, even the performance of the latest LLMs has plateaued as the demand for resources has grown and the focus has turned from creativity to accuracy.

The market is also maturing, often in response to a fear of Big Tech’s actions.

Legal arguments and even industrial action from authors, artists and composers are yet to settle over copyright material being used to train the LLMs. A similar concern has emerged regarding the exposure of confidential information unwittingly and perhaps irresponsibly included in the training data. (Expect to see new clauses in your NDAs soon.)

Schools, universities and the media are concerned about how to distinguish between real and artificial. A ‘perplexity score’ can be used to identify the author as human or artificial. The lower the score, the more likely the text is artificial. To err really is to be human after all.

The reality of creating, testing and operating LLMs is starting to sink in. Costs and resources are high. We are not yet at bitcoin levels of energy consumption, but carbon load is a reasonable concern along with business profitability. Techniques are emerging to shrink resources while maintaining performance.

We envisage some further rolling-back on GenAI claims and pushing right on the timelines over the coming months – even if the call to hit pause on GenAI development from Musk et al has fallen on deaf ears.

Although we are entering a period of understanding and potential regulation, the hype is still present. Just like bitcoin, there will always be businesses and media hitching otherwise prosaic concepts to the GenAI bandwagon.

5. Now is the time to explore GenAI for innovation

We can already see that GenAI is going to be transformative across market markets. A third or more of new software code is generated automatically. Consumers prefer talking with LLMs instead of waiting on a human call center. Legal arguments have been generated that have swayed the courts.

The tools to experiment, investigate and create testable proofs of concept are plentiful and the costs of doing so are modest. Innovation can be incremental, or it can be truly disruptive. The reality for most businesses is that GenAI will drive a bit of both. New tools will roll out to improve specific tasks and add value to the business today. Radical new approaches that change the entire business and reset the competitive landscape take longer. Their path is rarely straight, and exploration and feedback is vital. But the key to both is to get involved.

Despite what you might hear in the media, there is time to take a considered approach. The barrier is low and getting started now is the best way to reduce the risk of missing out on key commercial opportunities.

Interested in exploring how GenAI can accelerate your innovation?

In Part 2, we’ll share practical insights for the Product Owner in their quest to leverage GenAI.

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Trends in Respiratory

Trends in respiratory therapies: why pMDIs hang in the balance of new technology

September 25th 2023 6 min read
Steve Augustyn

Steve Augustyn 

Senior Consultant Mechanical Engineer

In May 2023, RDD Europe returned to a real-world conference after years of pandemic-enforced online-only presence. The location was spectacular – Antibes on the Cote d’Azur – with the sparkling Mediterranean Sea providing welcome relief from a dismal British spring.

The industry was well represented by device technology companies, CMOs, academics and pharma companies, and the presentations and workshops provided an engaging blend of research and practical advice.

Even though much of my time over the past ten years has been focused on parenteral device development, my career in combination products started in respiratory devices, working on a variety of dry powder inhaler (DPI) and pressurised metered dose inhaler (pMDI) devices, including the GSK Ellipta inhaler. This year at RDD, as I returned to my roots in this industry, three main themes struck me: preparing for the pMDI cliff edge; moving beyond traditional respiratory diseases; and implementing particle engineering for targeted treatment.

There were also two notable omissions: users and connectivity. More on those later.

Preparing for the cliff edge of pMDI propellants

The shift in pMDIs from using HFC propellants towards gases with a lower global warming potential (GWP) has gained momentum, with California imposing a ban on the sale and distribution of R227ea from the end of 2030, and R134a from the end of 2032, including for medical use. This means the end of the line for the sale of all current pMDI products in California, with other jurisdictions likely to follow suit as the world tries to move to a more sustainable solution.

The transition needs formulators, device designers, scientists, and other disciplines to collaborate to solve the challenges presented by the different physical properties of the new gases. Different thermodynamic and fluid dynamic properties can dramatically alter the plume geometry, droplet size and particle velocity, requiring careful redesign of the fluid pathways to compensate for the differences. These challenges were outlined in evidence presented by Recipharm (1), Proveris and Koura (2), and Healthy Airways LLC (3).

At Cambridge Design Partnership, we are receiving far fewer enquiries for pMDI products than DPIs and soft-mist inhalers. Obviously, an n=1 sample does not have a high degree of certainty, but it reflects a general sentiment among clients to focus future developments away from pMDI platforms.

Moving forward beyond traditional respiratory diseases

Asthma and COPD remain the biggest drivers in device and formulation development, much the same way that diabetes treatment has driven pen injector development. Two drivers that our drug delivery team have seen pushing device design in respiratory and the inhalation market are the need for home treatment, rather than hospital centered treatment; and platforms for biological drugs. The other significant drive is for vaccines that are stable at higher temperatures, which can be delivered without leaving behind copious volumes of blood-contaminated medical waste.

One challenge that comes with these new treatment regimens, beyond formulating drugs that will be stable in powder form, is getting the drug to the correct part of the body and making sure it remains present long enough to be effective. One paper from UCL and the University of Hong Kong (4) highlighted a promising approach to developing therapeutic antibodies against future SARS outbreaks. Some of these developments also require higher dose payloads, or API-only formulations; this presents a substantial challenge to device designers to make sure that the inhalation capabilities of different patient groups can achieve the required dose efficiency.

Aptar and Recipharm also shared their own device innovations to present novel spray and softmist technologies based on a syringe primary container. Targeting rapid treatment to the brain via the olfactory route is a much-neglected treatment option, in part due to the challenges of getting consistent behavior with users. At Cambridge Design Partnership, we’ve been working with a pioneering device company looking to exploit this pathway, and my colleague, Clare Beddoes, will be presenting information on this device development at PODD in October.

Enter: particle engineering for targeted treatment

In addition to the paper from UCL (4), particle engineering to target specific areas in the respiratory and nasal pathway was a topic that several posters and presentations addressed directly. Building on standard jet milling techniques, a paper from Aston University explained how isothermal dry particle coating (iDPC) can be used to create more potent formulations without increasing the volume of powder inhaled by the user (5). A third paper from Hovione and two Portuguese institutions focused on the characterization of different particle manufacturing techniques and how they affect deposition in nasal passages (6).

Closing the gap between the early stages of in vitro and in silico models, and the later stage in vivo performance, continues to receive a lot of attention. As the cost of computing power continues to fall, going into clinical or preclinical trials with greater confidence will accelerate time to market and reduce the cost burden on pharma companies looking to novel treatments.

Don’t forget user capability and connectivity

Two areas of development that received relatively little focus at the conference were human factors engineering (HFE) and connectivity – two concerns that are the subject of a great deal of effort in the parenteral sector. Recipharm presented a poster on the HFE advantages of their novel unit dose nasal spray when compared to a reference device (which bore a striking resemblance to an Aptar Unidose Liquid Nasal Spray). Research institution Solvias presented a paper showing how training users can lead to worse outcomes due to misperception of expertise using a device (7). This counterintuitive result demonstrated that patients with limited one-to-one training with a Handihaler showed more errors in use than patients who only had access to the device and IFU.

While these insights were welcome, our in-house team knows that patients continue to struggle to use inhalers reliably and consistently, leaving even the most effective drug products showing variable results.

These challenges for patient use are also being seen in the parenteral market, which is why we are working so closely with our clients to find better ways to train patients and leverage connectivity to improve adherence to medication regimens. These connectivity solutions are often in direct conflict with cost and sustainability targets and finding a route to square this circle is a challenge with which CDP’s designers and engineers are actively engaging.

See you in Tucson?

RDD 2023 was the first RDD conference I have attended. It was great to reconnect with former colleagues and make new connections across the industry. The conference was very well run, and the standard of papers and presentations ensured there was plenty of fascinating material for industry and academia to engage with. I’ve already blocked out my diary for RDD 2024 in Tucson and I look forward to seeing you there.

References

      1. Albuterol Sulfate Metered Dose Inhaler Feasibility Using an Environment Friendly Propellant HFA152a and Novel Valves (Lei Mao, Sheryl Johnson, Nischal Pant, James Murray, Donald Ellis, Benjamin Zechinati, Johnathan Carr and Victoria Cruttenden)

      1. Comparison of Spray Characteristics of P-134a and Low GWP P-152a pMDIs With and Without Ethanol (Lynn Jordan, Sheryl Johnson, Ramesh Chand, Grant Thurston, Deborah Jones, Vanessa Webster and Sally Stanford)

      1. Accelerated Development of MDIs with Low GWP Propellants in a QbD Era: Practical, Regulatory and Scientific Considerations (Healthy Airways LLC and First Flight Pharma LLC)

      1. Inhaled Antibody Therapies: Enabling Prophylactic Protection against SARS-CoV-2 Infection with a Dual Targeting Powder Formulation (Han Song Saw and Jenny Ka-Wing Lam)

      1. Use of Isothermal Dry Particle Coating (iDPC) for the Development of High Dose Dry Powder Inhalers (Jasdip S. Koner, David A. Wyatt, Amandip S. Gill, Shital Lungare, Rhys Jones and Afzal R. Mohammed)

      1. Benchmarking of Particle Engineering Strategies for Nasal Powder Delivery: Characterization of Nasal Deposition Using the Alberta Idealized Nasal Inlet (Patricia Henriques, Cláudia Costa, António Serôdio, Ana Fortuna, and Slavomíra Doktorovová)

      1. Effect of Capsule-Based Dry Powder Inhaler User Training on In Vitro Performance (Oleksandra Troshyna and Yannick Baschung)

Connect with CDP

For more on how to navigate the evolving respiratory device landscape, from propellant transitions to targeted delivery, contact Cambridge Design Partnership.

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